# Detecting Horizontal Gene Transfer by Mapping Sequencing Reads Across Species Boundaries

## TL;DR

This paper introduces Daisy, a new tool that detects horizontal gene transfer directly from sequencing data, enabling faster identification of genetic material transfer between species.

## Contribution

Daisy is a novel mapping-based tool for detecting horizontal gene transfer directly from NGS data, offering higher resolution and performance compared to existing methods.

## Key findings

- Daisy successfully detects HGT regions with base pair resolution in both simulated and real data.
- Daisy outperforms alternative approaches that rely on genome assembly of sequencing reads.
- The tool provides a powerful complement for analyzing HGT in next-generation sequencing contexts.

## Abstract

Horizontal gene transfer (HGT) is a fundamental mechanism that enables organisms such as bacteria to directly transfer genetic material between distant species. This way, bacteria can acquire new traits such as antibiotic resistance or pathogenic toxins. Current bioinfor-matics approaches focus on the detection of past HGT events by exploring phylogenetic trees or genome composition inconsistencies. However, this normally requires the availability of finished and fully annotated genomes and of sufficiently large deviations that allow detection. Thus, these techniques are not widely applicable. Especially in an outbreak scenario where new HGT mediated pathogens emerge, there is need for fast and precise HGT detection. Next-generation sequencing (NGS) technologies can facilitate swift analysis of unknown pathogens but, to the best of our knowledge, so far no approach uses NGS data directly to detect HGTs.

We present Daisy, a novel mapping-based tool for HGT detection directly from NGS data. Daisy determines HGT boundaries with split-read mapping and evaluates candidate regions relying on read pair and coverage information. Daisy can successfully detect HGT regions with base pair resolution in both simulated and real data, and outperforms alternative approaches using a genome assembly of the reads. We see our approach as a powerful complement for a comprehensive analysis of HGT in the context of NGS data. Daisy is freely available from http://github.com/ktrappe/daisy.